In general robotic excavation investigations have focused around digging, weight estimation and motion planning. Singh [11] provides a good review of the field and discusses state-of-the-art in sensing and machine/ground interaction models. He then uses a number of implemented systems as examples to illustrate different levels of autonomy: teleoperation, trajectory control, tactical and strategic planning.
A significant body of relevant work was conducted at Carnegie-Mellon University by Singh, Cannon, Rowe, Stentz and others in the 1990s. Cannon [1] conducted a comprehensive study and experimental evaluation of an autonomous 25 tonne hydraulic backhoe type excavator.
Australian Patent Application No 199895225 (Carnegie Mellon University) [13] describes a template-based loading strategy using perceptual feedback. In this patent a digital map of the height and shape of the load in a partially loaded truck is processed, together with templates that represent the ideal distribution of the load. In particular the map and template are similarly gridded and a correlation is calculated between the map and template for at least some of the cells of the grid. The optimal location for the next dump into the truck is selected using the results of the correlation calculations.
In earlier work related to automating dragline dig-dump-dig motion. One system was evaluated in a production trial which moved 250,000 tonnes of material in a two week period [12]. In addition to the dragline control system, other technologies have been developed, including digital terrain mapping and a safe and intuitive human-machine control interface. In 2005, this work was extended to include autonomous digging with complete autonomous dragline excavation and dumping operations demonstrated [4].